Image: Carrie Klein, doctoral candidate in the Higher Education Program at George Mason University, visited the Institute on Wednesday, April 24. Her talk, Analytics in Higher Education: Complexities in Design, Implementation, and Efficacy, examined predictive learning analytic tools. Given the hype surrounding these tools' effectiveness in improving student learning, retention, and completion rates and the proliferation of commercial products, Klein advises education administrators to investigate these services carefully. She acknowledges the appeal of these tools as education administrators seek to demonstrate institutional responsiveness by measuring and quantifying student outcomes and by monitoring key flags. As a result, learning analytic software suites are being purchased and deployed at increasing rates across institutional types. She outlines four major areas in which significant barriers exist to implementation: technological, organizational, individual, and ethical. Klein urges that the ways in which the data can be culled, repurposed, and reused requires an intentional and clear approach to data governance, protection, and use. Before an institution implements, it should be clear about the limitations of the data, its internal data governance and protection policies, the level of data literacy and statistics acumen of its employees, and guarantees of personal data rights and care of data by third parties. Overall, Klein takes a measured approach, recognizing the usefulness of analytics to show emerging trends and to deliver tailored feedback from large and varied datasets while underscoring the potential ways that big data can be misinterpreted, misused, and misappropriated. She maintains that these tools are for forecasting general trends, not decision-making, and that people familiar with the strengths and limitations of the data should use the indicators as a starting point of conversation rather than a final verdict. Type of News/Audience: General News
